|Publication number||US6317787 B1|
|Application number||US 09/132,287|
|Publication date||Nov 13, 2001|
|Filing date||Aug 11, 1998|
|Priority date||Aug 11, 1998|
|Also published as||CA2340109A1, CA2340109C, DE69937249D1, DE69937249T2, EP1131725A1, EP1131725A4, EP1131725B1, WO2000010093A1|
|Publication number||09132287, 132287, US 6317787 B1, US 6317787B1, US-B1-6317787, US6317787 B1, US6317787B1|
|Inventors||William Glen Boyd, Elijahu Shapira|
|Original Assignee||Webtrends Corporation|
|Export Citation||BiBTeX, EndNote, RefMan|
|Patent Citations (22), Non-Patent Citations (3), Referenced by (181), Classifications (14), Legal Events (9)|
|External Links: USPTO, USPTO Assignment, Espacenet|
This invention relates generally to web-server traffic data analysis and more particularly to a system and method for analyzing web-server log files.
The worldwide web (hereinafter “web”) is rapidly becoming one of the most important publishing mediums today. The reason is simple: web servers interconnected via the Internet provide access to a potentially worldwide audience with a minimal investment in time and resources in building a web site. The web server makes available for retrieval and posting a wide range of media in a variety of formats, including audio, video and traditional text and graphics. And the ease of creating a web site makes reaching this worldwide audience a reality for all types of users, from corporations, to startup companies, to organizations and individuals.
Unlike other forms of media, a web site is interactive and the web server can passively gather access information about each user by observing and logging the traffic data packets exchanged between the web server and the user. Important facts about the users can be determined directly or inferentially by analyzing the traffic data and the context of the “hit.” Moreover, traffic data collected over a period of time can yield statistical information, such as the number of users visiting the site each day, what countries, states or cities the users connect from, and the most active day or hour of the week. Such statistical information is useful in tailoring marketing or managerial strategies to better match the apparent needs of the audience. Each hit is also encoded with the date and time of the access. Because the statistical information of interest is virtually all related to time periods, accurately tracking the time of each hit is critical.
To optimize use of this statistical information, web server traffic analysis must be timely. However, it is not unusual for a web server to process thousands of users daily. The resulting access information recorded by the web server amounts to megabytes of traffic data. Some web servers generate gigabytes of daily traffic data. Analyzing the traffic data for even a single day to identify trends or generate statistics is computationally intensive and time-consuming. Moreover, the processing time needed to analyze the traffic data for several days, weeks or months increases linearly as the time frame of interest increases.
The problem of performing efficient and timely traffic analysis is not unique to web servers. Rather, traffic data analysis is possible whenever traffic data is observable and can be recorded in a uniform manner, such as in a distributed database, client-server system or other remote access environment.
Some web servers are so busy, i.e., handle so much traffic, that they require multiple servers to handle all of the traffic. Other users may need to employ multiple servers because of the large size of the web site. Critical sites, i.e., ones that cannot afford to be down because of a problem with a server, may also choose to deploy their site on multiple servers. Such multiple servers are sometimes referred to as a server farm. Server farms provide high bandwidth reliable access to web sites.
There are several topologies that may be used in a server farm, but the most important ones divide the farm into clusters of servers. The web site is mirrored on each server within the cluster. Special hardware receives all of the traffic to the web site and distributes each hit to one of the servers. Some systems provide accurate load balancing in that all of the hits are rotated in sequence among each of the servers. But others assign each hit from a new source to a server, and further access to the site from that source is directed to the assigned server. This is accomplished by assigning a predetermined time period, for example 30 minutes, during which all future access from the same source is considered to be part of a single session from that source. As described further below, the latter approach permits some log-file analysis, which is not possible using the load-balancing technique.
Server farms, although providing load balancing and redundancy, present problems in analyzing the log files generated by the servers. Prior art systems for analyzing web-server log files can handle multiple log files, but these files are consecutively generated, i.e., the data packets within each log file are in chronological order and the log files themselves correspond to time periods containing data packets from within the periods. In other words, the log files are also consecutively generated. Log files on servers in a server farm, however, are concurrently generated. Each log file covers or overlaps the same time period. On server farms that rotate the hits among each server, log file analysis programs do not generate useful information. Brute force solutions are possible, such as sorting all of the log files and creating a new single file, or copying all of the hits from each log file to a large database, which can sort and analyze the data. These solutions have severe drawbacks: they are computationally intensive, they require creation of large new files, and they are done only after log files are complete, i.e., not on the fly while the log file is still being populated.
Server farms that assign hits from a new source to a single user can run prior art log analysis programs on each server and sum the results. This, however, is not completely accurate and is disadvantageous because it requires generation of separate reports that must each be consulted or further manipulated to obtain information that applies to the entire server farm.
There is consequently a need for a system and method for analyzing web-server log files that are concurrently generated, such as those generated by a server farm.
There is a further need for such a system and method that can analyze the log files substantially in real time.
There is still a further need for such a system that can analyze the log files without generating new large files and without the need for substantial additional computing power.
There is also a need for such a system that can analyze log files whether they are concurrently or consecutively generated.
The present invention comprises a method for analyzing log files containing a plurality of data packets in sequence comprising: (a) selecting the first data packet in each log file; (b) comparing the selected data packets; (c) passing the oldest of the selected data packets to a log file analyzer; (d) selecting the next data packet in the log file in which the passed data packet was selected; and (e) repeating steps (b) through (d) until all of the data packets in the log files are passed.
The foregoing and other features and advantages of the invention will become more readily apparent from the following detailed description of a preferred embodiment of the invention, which proceeds with reference to the accompanying drawings.
FIG. 1 is a functional block diagram of a prior art system for analyzing traffic data in a distributed computing environment according to the present invention.
FIG. 2 is a flow diagram of a prior art method for analyzing traffic data in a distributed computing environment according to the present invention using the system of FIG. 1.
FIG. 3A shows a prior art format used in storing a “hit” of traffic data received by the server of FIG. 1.
FIG. 3B shows, by way of example, a “hit” of formatted traffic data received by the server of FIG. 1.
FIG. 4 is a schematic diagram of a server farm, including multiple servers like that shown and described in FIG. 1.
FIG. 5 is a schematic diagram illustrating the operation of the server farm of FIG. 4.
FIG. 6 is a schematic diagram illustrating the present invention implemented in the server farm of FIG. 4.
FIG. 7 is a schematic diagram similar to FIG. 6, but illustrating the present invention operating on consecutive log files.
FIG. 8 is a flow diagram of a routine for implementing the present invention.
FIG. 1 is a functional block diagram of a prior art system for analyzing traffic data in a distributed computing environment 9. It is more fully described in “WebTrends Installation and User Guide,” version 2.2, October 1996, and in U.S. patent application Ser. No. 08/801,707, now U.S. Pat. No. 6,112,238, the disclosures of which are incorporated herein by reference. WebTrends is a trademark of Webtrends Corporation, Portland, Oreg.
A server 10 provides web site and related services to remote users. By way of example, the remote users can access the server 10 from a remote computer system 12 interconnected with the server 10 over a network connection 13, such as the Internet or an intranetwork, a dial up (or point-to-point) connection 14 or a direct (dedicated) connection 17. Other types of remote access connections are also possible.
Each access by a remote user to the server 10 results in a “hit” of raw traffic data 11. The format used in storing each traffic data hit 11 and an example of a traffic data hit 11 are described below with reference to FIGS. 3A and 3B, respectively. The server 10 preferably stores each traffic data hit 11 in a log file 15, although a database 16 or other storage structure can be used.
To analyze the traffic data, the server 10 examines each traffic data hit 11 and stores the access information obtained from the traffic data as analysis results 18A-C. Five sources of traffic data 11 (remote system 12, dial-up connection 14, log file 15, database 16 and direct connection 17) are shown. Other sources are also possible. The traffic data hits 11 can originate from any single source or from a combination of these sources. While the server 10 receives traffic data hits 11 continuously, separate sets of analysis results 18A-C are stored for each discrete reporting period, called a time slice. The analysis results 18A-C are used for generating summaries 19A-C of the access information.
In the described embodiment, the server 10 is typically an Intel Pentium-based computer system equipped with a processor, memory, input/output interfaces, a network interface, a secondary storage device and a user interface, preferably such as a keyboard and display. The server 10 typically operates under the control of either the Microsoft Windows NT or Unix operating systems and executes either Microsoft Internet Information Server or NetScape Communications Server software. Pentium, Microsoft, Windows, Windows NT, Unix, Netscape and Netscape Communications Server are trademarks of their respective owners. However, other server 10 configurations varying in hardware, such as DOS-compatible, Apple Macintosh, Sun Workstation and other platforms, in operating systems, such as MS-DOS, Unix and others, and in web software are also possible. Apple, Macintosh, Sun and MS-DOS are trademarks of their respective owners.
FIG. 2 is a flow diagram of a method 20 for analyzing traffic data in a distributed computing environment according to the present invention using the system of FIG. 1. Its purpose is to continuously collect and summarize access information from traffic data hits 11 while allowing on-demand, ad hoc analyses. The method 20 consists of two routines. Access information is collected from traffic data hits 11 and summarized by the server 10 into analysis results 18A-C (block 21). The access information is separately analyzed for generating the summaries 19A-C which identify trends, statistics and other information (block 22). The collection and summarizing of the access information (block 21) is performed continuously by the server 10 while the analysis of the access information (block 22) is performed on an ad hoc basis by either the server 10 or a separate workstation (not shown).
The method 20 is preferably implemented as a computer program executed by the server 10 and embodied in a storage medium comprising computer-readable code. In the described embodiment, the method 20 is written in the C programming language, although other programming languages are equally suitable. It operates in a Microsoft Windows environment and can analyze Common Log File, Combined Log File and proprietary log file formats from industry standard web servers, such as those licensed by NetScape, NCSA, O'Reilly WebSite, Quarterdeck, C-Builder, Microsoft, Oracle, EMWAC, and other Windows 3.x, Windows NT 95, Unix and Macintosh Web servers. The analysis results 18A-C can be stored in a proprietary or standard database 16 (shown in FIG. 1), such as SQL, BTRIEVE, ORACLE, INFORMIX and others. The method 20 uses the analysis results 18A-C of traffic data hits 11 as collected into the log file 15 or database 16 for building activity, geographic, demographic and other summaries 19A-C, such as listed below in Table 1. Other summaries 19A-C are also possible.
User Profile by Regions
General Statistics Table
Top Requested Pages
Least Requested Pages
Top Entry Pages
Top Exit Pages
Single Access Pages
Top Paths Through Site
Advertising Views and Clicks
Most Downloaded Files
Most Active Organizations
Most Active Countries
Activity Summary by Day of Week
Activity Summary by Day
Activity Summary by Hour of the Day
Activity Summary Level by
Hours of the Day
Web Server Statistics and Analysis
Top Downloaded File Types and Sizes
Activity by Organization Type
Top Directories Accessed
Top Referring Sites
Top Referring URLs
Microsoft Explorer Browsers
In addition, the analysis results 18A-C can be used for automatically producing reports and summaries which include statistical information and graphs showing, by way of example, user activity by market, interest level in specific web pages or services, which products are most popular, whether a visitor has a local, national or international origin and similar information. In the described embodiment, the summaries 19A-C can be generated as reports in a variety of formats. These formats include hypertext markup language (HTML) files compatible with the majority of popular web browsers, proprietary file formats for use with word processing, spreadsheet, database and other programs, such as Microsoft Word, Microsoft Excel, ASCII files and various other formats. Word and Excel are trademarks of Microsoft Corporation, Redmond, Wash.
FIG. 3A shows a format used in storing a “hit” of raw traffic data 11 received by the server of FIG. 1. A raw traffic data hit 11 is not in the format shown in FIG. 3A. Rather, the contents of each field in the format is determined from the data packets exchanged between the server 10 and the source of the traffic data hit 11 and the information pulled from the data packets is stored into a data record using the format of FIG. 3A prior to being stored in the log file 15 (shown in FIG. 1) or processed.
Each traffic data hit 11 is a formatted string of ASCII data. The format is based on the standard log file format developed by the National Computer Security Association (NCSA), the standard logging format used by most web servers. The format consists of seven fields as follows:
Internet protocol (IP) address or domain name of the
user accessing the site.
Obsolete field usually left blank, but increasingly used
by many web servers to store the host domain name
for multi-homed log flles.
Exchanges the user name if required for access to the
Date and time of the access and the time offset from
Either GET (a page request) or POST (a form
Return status of the request which specifies whether the
transfer was successful.
Number of bytes transferred for the file request, that is,
the file size.
In addition, three optional fields can be employed as follows:
Referring Site (37):
URL used to obtain web site information for
performing the “hit.”
Browser version, including make, model or version
number and operating system.
Unique identifier pemiissively used to identify a
Other formats of traffic data hits 11 are also possible, including proprietary formats containing additional fields, such as time to transmit, type of service operation and others. Moreover, modifications and additions to the formats of raw traffic data hits 11 are constantly occurring and the extensions required by the present invention to handle such variations of the formats would be known to one skilled in the art.
FIG. 3B shows, by way of example, a “hit” of raw traffic data received by the server of FIG. 1. The user address 30 field is “tarpon.gulf.net” indicating the user originates from a domain named “gulf.net” residing on a machine called “tarpon.” The RFC931 31 and user authorization 32 fields are “−” indicating blank entries. The Date/Time 33 field is “Jan. 12, 1996:20:38:17+0000” indicating an access on Jan. 12, 1996 at 8:38:17 pm GMT. The Request 34 field is “GET/general.htm HTTP/1.0” indicating the user requested the “general.htm” page. The Return Code 35 and Transfer Size 36 fields are 200 and 3599, respectively, indicating a successful transfer of 3599 bytes.
Turning now to FIG. 4, indicated generally at 40 is a server farm constructed in accordance with the present invention. Included therein are two server clusters 42, 44, each of which includes servers 46, 48, 50 and servers 50, 52, 54, respectively. Each of the servers in clusters 42, 44 are substantially identical to server 10 in FIG. 1. In the present embodiment, server cluster 42 hosts a first web site, which is mirrored on each of the servers therein, at a single identified Internet Protocol (IP) address. The servers in cluster 44 host a second web site, which is mirrored on each of the servers therein, at a second identified IP address.
Each of the servers in clusters 42, 44 is connected via a cable, like cable 58, to a redirector 60. The redirector in turn receives an input from a network connection 62, which in the present embodiment is an Internet connection. Redirector 60 is a prior art hardware device that receives a source of traffic data hits—in the present case, via connection 62—and distributes them to the servers in clusters 42, 44.
In the present implementation, redirector 60 distributes traffic data hits within each of clusters 42, 44. In other words, the traffic data hits generated as a result of access to the web site posted on cluster 42, are distributed among servers 46, 48, 50. Similarly, traffic data hits produced by accessing the web site on cluster 44 are distributed among servers 52, 54, 56. One device suitable for functioning as a redirector is manufactured by Cisco Systems and sold under the name LocalDirector. Those having skill in the art will appreciate that other known hardware devices can perform the function of redirector 60.
Turning now to FIG. 5, log files 46A, 48A, 50A, are each stored on the server corresponding to the numeral used to indicate the log file. These log files are generated and stored in the manner described in connection with the server of FIG. 1. In FIG. 5, the hits are numbered sequentially, hit number 1 through hit number 13 in the chronological order in which each traffic data hit was generated. In the depiction of FIG. 5, each of log files 46A, 48A, 50A is still being added to. That is in log file 46A, for example, hit number 1 is the first-stored data hit, and hit number 5 is the next-stored data hit, with hits numbers 6 and 12 being thereafter stored in sequence. Because log file 46A is not yet full and it remains open, additional hits may be stored in sequence after hit number 12. The same is true for logs file 48A, 50A.
Turning now to FIG. 6, included therein is a sorter 64, which examines the hits in sequence in each of the log files and passes them—in the chronological order in which each hit was generated—to a log file analyzer 66. The log file analyzer operates generally as described in connection with the server depicted in FIG. 1. Thereafter, analysis results are passed to analysis results 18A-C, also as described in connection with FIG. 1.
The operation of sorter 64 can best be understood with reference to the following Table 2 and to the flow diagram depicted in FIG. 8.
First, in block 68 of FIG. 8, the first record received in each log file 46A, 48A, 50A is selected. This selection is depicted in Table 2, line 1, in which hits 1, 2, and 4 appear in the Compare column. In block 70, sorter 64 compares each of hits 1, 2, and 4 and passes the oldest (in time) record, namely hit 1 (block 72). The routine next determines, in block 74, whether all the records in all of the log files have been selected, compared, and passed. If so, the routine ends in block 76. If not, in block 78 the routine selects the next record in the log file containing the record that was passed in block 72. In the example currently under consideration, the next record is hit number 5 in log file 46A. Next—with reference to line 2 of Table 2—in block 70, hits 5, 2 and 4 are compared, and hit 2 is passed, it being the oldest (in time) of the three records compared.
Because the routine operates in first-in, first-out (FIFO) sequence on each of the log files, it can process while the files remain open and continue to receive additional hits in sequence.
In the example of FIG. 7, log files 80, 82, 84 are operated on by sorter 64. It should be noted that these log files include hits that are in sequential chronological order. What is more, each of the log files is generated in chronological order. Thus, log file 80 represents an identified time period, ranging between the time associated with hit 1 and the time of hit 4; log file 82, ranging between the times of hits 5 and 8; and log file 84, between hits 9 and 12. With reference again to FIG. 8, and to Table 3, which depicts the sequential comparisons made on the log-file records in FIG. 7, hits 1, 5 and 9 are selected in block 68 and compared in block 70. Hit 1, the oldest record, is passed in block 72 and the next-in record, hit number 2, is selected in block 78. This sequence continues until all of hits 1 through 12 are passed, hits 1 through 4 being first passed in sequence from log file 80, hits 5 through 8 being next passed in sequence from log file 82, and finally hits 9 through 12 in sequence from log file 84.
The present invention therefore properly sorts traffic data hits in either concurrently-generated or consecutively-generated log files. This is advantageous because it obviates the need for separate routines or for configuring a program dependant upon whether the log files are consecutive or concurrent. In addition, the present invention is capable of sorting log files while they continue to receive and store new traffic data hits. This analysis on the fly provides users with statistical data and reports on a near real time basis.
Numerous modifications and embodiments of the invention will be obvious to those skilled in the art. Although the present invention has been described in terms of one embodiment, this should not be interpreted as limiting. Various alterations, modifications and combinations will no doubt become apparent to those skilled in the art after having read the above disclosure. Accordingly, the appended claims should be interpreted as covering all alterations and modifications that fall within the spirit and scope of the invention.
|Cited Patent||Filing date||Publication date||Applicant||Title|
|US5600632 *||Mar 22, 1995||Feb 4, 1997||Bell Atlantic Network Services, Inc.||Methods and apparatus for performance monitoring using synchronized network analyzers|
|US5675510||Jun 7, 1995||Oct 7, 1997||Pc Meter L.P.||Computer use meter and analyzer|
|US5689416||Jun 1, 1995||Nov 18, 1997||Fujitsu Limited||Computer system monitoring apparatus|
|US5706436 *||Feb 1, 1995||Jan 6, 1998||Cabletron Systems, Inc.||Apparatus and method for evaluation network traffic performance|
|US5727129||Jun 4, 1996||Mar 10, 1998||International Business Machines Corporation||Network system for profiling and actively facilitating user activities|
|US5732218||Jan 2, 1997||Mar 24, 1998||Lucent Technologies Inc.||Management-data-gathering system for gathering on clients and servers data regarding interactions between the servers, the clients, and users of the clients during real use of a network of clients and servers|
|US5774660 *||Aug 5, 1996||Jun 30, 1998||Resonate, Inc.||World-wide-web server with delayed resource-binding for resource-based load balancing on a distributed resource multi-node network|
|US5774716 *||Jun 5, 1995||Jun 30, 1998||International Business Machines Corp.||Computer program product to enable multiple computer systems to share single sequential log|
|US5787253 *||May 28, 1996||Jul 28, 1998||The Ag Group||Apparatus and method of analyzing internet activity|
|US5796942 *||Nov 21, 1996||Aug 18, 1998||Computer Associates International, Inc.||Method and apparatus for automated network-wide surveillance and security breach intervention|
|US5796952 *||Mar 21, 1997||Aug 18, 1998||Dot Com Development, Inc.||Method and apparatus for tracking client interaction with a network resource and creating client profiles and resource database|
|US5870559 *||Apr 11, 1997||Feb 9, 1999||Mercury Interactive||Software system and associated methods for facilitating the analysis and management of web sites|
|US5878223||May 7, 1997||Mar 2, 1999||International Business Machines Corporation||System and method for predictive caching of information pages|
|US5892917 *||Sep 27, 1995||Apr 6, 1999||Microsoft Corporation||System for log record and log expansion with inserted log records representing object request for specified object corresponding to cached object copies|
|US5898837 *||Sep 27, 1996||Apr 27, 1999||Bay Networks, Inc.||Method and apparatus for monitoring a dedicated communications medium in a switched data network|
|US5913041 *||Dec 9, 1996||Jun 15, 1999||Hewlett-Packard Company||System for determining data transfer rates in accordance with log information relates to history of data transfer activities that independently stored in content servers|
|US5968125 *||Jan 21, 1997||Oct 19, 1999||Net. Roi||Process for optimizing the effectiveness of a hypertext element|
|US6023744 *||Mar 7, 1997||Feb 8, 2000||Microsoft Corporation||Method and mechanism for freeing disk space in a file system|
|US6055572 *||Jan 20, 1998||Apr 25, 2000||Netscape Communications Corporation||System and method for creating pathfiles for use to predict patterns of web surfaces|
|US6098079 *||Apr 2, 1998||Aug 1, 2000||Mitsubishi Electric Information Technology Center America, Inc. (Ita)||File version reconciliation using hash codes|
|US6112238 *||Feb 14, 1997||Aug 29, 2000||Webtrends Corporation||System and method for analyzing remote traffic data in a distributed computing environment|
|US6175838 *||Apr 29, 1998||Jan 16, 2001||Ncr Corporation||Method and apparatus for forming page map to present internet data meaningful to management and business operation|
|1||*||SPI, Dataflow Sorting [From V Programming]p.140-143 by Wade Wialliam and Aschcrof Edward, Jan. 1985.|
|2||WebTrends(TM) Essential Reporting for your Web Server, Installation and User Guide, Jan. 1996 Edition, by e.g. Software, Inc., 62 page manual.|
|3||WebTrends™ Essential Reporting for your Web Server, Installation and User Guide, Jan. 1996 Edition, by e.g. Software, Inc., 62 page manual.|
|Citing Patent||Filing date||Publication date||Applicant||Title|
|US6477483 *||Jan 17, 2000||Nov 5, 2002||Mercury Interactive Corporation||Service for load testing a transactional server over the internet|
|US6493754 *||Nov 5, 1998||Dec 10, 2002||Computer Associates Think, Inc.||Method and apparatus for analyzing communications on different threads|
|US6529952 *||Apr 2, 1999||Mar 4, 2003||Nielsen Media Research, Inc.||Method and system for the collection of cookies and other information from a panel|
|US6560564||Jul 8, 2002||May 6, 2003||Mercury Interactive Corporation||System and methods for load testing a transactional server over a wide area network|
|US6564170 *||Dec 29, 2000||May 13, 2003||Hewlett-Packard Development Company, L.P.||Customizable user interfaces|
|US6574627 *||Feb 24, 1999||Jun 3, 2003||Francesco Bergadano||Method and apparatus for the verification of server access logs and statistics|
|US6651141||Dec 29, 2000||Nov 18, 2003||Intel Corporation||System and method for populating cache servers with popular media contents|
|US6654699 *||Dec 29, 2000||Nov 25, 2003||Microsoft Corporation||Computer network testing system and method using client playback of edited network information|
|US6658374 *||Apr 16, 2001||Dec 2, 2003||Sony Corporation||Maintenance support system for electronic apparatus|
|US6662227 *||Jan 11, 2002||Dec 9, 2003||Netiq Corp||System and method for analyzing remote traffic data in a distributed computing environment|
|US6728932 *||Mar 22, 2000||Apr 27, 2004||Hewlett-Packard Development Company, L.P.||Document clustering method and system|
|US6760763||Aug 27, 1999||Jul 6, 2004||International Business Machines Corporation||Server site restructuring|
|US6763321 *||Jun 22, 2001||Jul 13, 2004||Sun Microsystems, Inc.||Method and apparatus to facilitate measurement of quality-of-service performance of a network server|
|US6789115 *||Jul 9, 1999||Sep 7, 2004||Merrill Lynch & Company||System for collecting, analyzing, and reporting high volume multi-web server usage|
|US6792458 *||Oct 4, 2000||Sep 14, 2004||Urchin Software Corporation||System and method for monitoring and analyzing internet traffic|
|US6850944 *||Nov 20, 2000||Feb 1, 2005||The University Of Alabama||System, method, and computer program product for managing access to and navigation through large-scale information spaces|
|US6904461 *||Oct 19, 2000||Jun 7, 2005||Unitel Technologies, Inc.||Method for making time-sensitive determinations of traffic intensity for a visitable site|
|US6931357 *||Jul 18, 2003||Aug 16, 2005||Computer Network Technology Corp.||Computer network monitoring with test data analysis|
|US6968341 *||May 25, 2001||Nov 22, 2005||International Business Machines Corporation||System and method for post-analyzing, and sequentially visualizing plurality of predefined metrics in a stored dynamic data values associated identifiers within determined time range|
|US7003565 *||Apr 3, 2001||Feb 21, 2006||International Business Machines Corporation||Clickstream data collection technique|
|US7020709||Jun 30, 2000||Mar 28, 2006||Intel Corporation||System and method for fault tolerant stream splitting|
|US7035926 *||Nov 30, 1999||Apr 25, 2006||International Business Machines Corporation||Real-time monitoring of web activities|
|US7181678||Jan 29, 2004||Feb 20, 2007||Hewlett-Packard Development Company, L.P.||Document clustering method and system|
|US7206838 *||Oct 17, 2003||Apr 17, 2007||Webtrends Corporation||System and method for analyzing remote traffic data in a distributed computing environment|
|US7225246 *||Aug 21, 2001||May 29, 2007||Webtrends, Inc.||Data tracking using IP address filtering over a wide area network|
|US7254784||Aug 13, 2003||Aug 7, 2007||Peter H. Chang||User-driven menu generation system with multiple submenus|
|US7318107||Jun 30, 2000||Jan 8, 2008||Intel Corporation||System and method for automatic stream fail-over|
|US7320007||Dec 7, 2004||Jan 15, 2008||Peter Hon-You Chang||Dynamic generation of target files from template files and tracking of the processing of target files|
|US7401057||Nov 19, 2003||Jul 15, 2008||Asset Trust, Inc.||Entity centric computer system|
|US7454705 *||Apr 22, 2004||Nov 18, 2008||Microsoft Corporation||Cluster-based visualization of user traffic on an internet site|
|US7523190 *||Dec 23, 1999||Apr 21, 2009||Bickerstaff Cynthia L||Real-time performance assessment of large area network user experience|
|US7558854||Mar 7, 2003||Jul 7, 2009||Hitachi, Ltd.||Access relaying apparatus|
|US7590724 *||Apr 23, 2003||Sep 15, 2009||Williams Robert D||Auto instrumentation system|
|US7594012 *||Aug 13, 2004||Sep 22, 2009||Merrill Lynch & Company||System for collecting, analyzing, and reporting high volume multi-web server usage|
|US7603373||Nov 18, 2004||Oct 13, 2009||Omniture, Inc.||Assigning value to elements contributing to business success|
|US7624176||Oct 14, 2004||Nov 24, 2009||International Business Machines Corporation||Method and system for programmatically generating synthetic transactions to monitor performance and availability of a web application|
|US7650407||Dec 29, 2006||Jan 19, 2010||The Nielsen Company (Us), Llc.||Content display monitor|
|US7653724||Dec 29, 2006||Jan 26, 2010||The Nielsen Company (Us), Llc.||Content display monitor|
|US7716326||Dec 29, 2006||May 11, 2010||The Nielsen Company (Us), Llc.||Content display monitor|
|US7720963||Dec 29, 2006||May 18, 2010||The Nielsen Company (Us), Llc||Content display monitor|
|US7720964||Dec 29, 2006||May 18, 2010||The Nielsen Company (Us), Llc||Content display monitor|
|US7734772||Mar 5, 2007||Jun 8, 2010||Webtrends, Inc.||System and method for analyzing remote traffic data in a distributed computing environment|
|US7752308||Oct 30, 2007||Jul 6, 2010||Hutchinson Kevin P||System for measuring web traffic|
|US7756974||Dec 29, 2006||Jul 13, 2010||The Nielsen Company (Us), Llc.||Content display monitor|
|US7792827 *||Dec 31, 2002||Sep 7, 2010||International Business Machines Corporation||Temporal link analysis of linked entities|
|US7792954||Oct 15, 2004||Sep 7, 2010||Webtrends, Inc.||Systems and methods for tracking web activity|
|US7822850 *||Jan 11, 2008||Oct 26, 2010||Cisco Technology, Inc.||Analyzing log files|
|US7853684 *||Oct 15, 2002||Dec 14, 2010||Sas Institute Inc.||System and method for processing web activity data|
|US7941394 *||Dec 20, 2005||May 10, 2011||Adobe Systems Incorporated||User interface providing summary information or a status pane in a web analytics tool|
|US7953791||Apr 10, 2008||May 31, 2011||The Nielsen Company (Us), Llc.||Network resource monitoring and measurement system and method|
|US7953839||May 15, 2010||May 31, 2011||The Nielsen Company (Us), Llc.||Network resource monitoring and measurement system and method|
|US7970640||Jun 12, 2002||Jun 28, 2011||Asset Trust, Inc.||Purchasing optimization system|
|US7991640 *||Aug 23, 2007||Aug 2, 2011||Webtrends Inc.||Method and apparatus for evaluating visitors to a web server|
|US7991732||Dec 20, 2005||Aug 2, 2011||Adobe Systems Incorporated||Incrementally adding segmentation criteria to a data set|
|US8024463||Jul 28, 2010||Sep 20, 2011||Webtrends, Inc.||Systems and methods for tracking web activity|
|US8056092||Sep 29, 2006||Nov 8, 2011||Clearspring Technologies, Inc.||Method and apparatus for widget-container hosting and generation|
|US8112511||Apr 29, 2011||Feb 7, 2012||The Nielsen Company (Us), Llc||Network resource monitoring and measurement system and method|
|US8127007||Aug 18, 2011||Feb 28, 2012||Webtrends, Inc.||Systems and methods for tracking web activity|
|US8166157||Mar 23, 2007||Apr 24, 2012||Fmr Llc||Enterprise application performance monitors|
|US8185486||Jan 20, 2009||May 22, 2012||Asset Trust, Inc.||Segmented predictive model system|
|US8195794||May 5, 2010||Jun 5, 2012||Webtrends, Inc.||System and method for analyzing remote traffic data in a distributed computing environment|
|US8209378||Oct 2, 2008||Jun 26, 2012||Clearspring Technologies, Inc.||Methods and apparatus for widget sharing between content aggregation points|
|US8266274||Mar 6, 2008||Sep 11, 2012||Clearspring Technologies, Inc.||Method and apparatus for data processing|
|US8271778||Jul 24, 2002||Sep 18, 2012||The Nielsen Company (Us), Llc||System and method for monitoring secure data on a network|
|US8280873 *||Apr 17, 2002||Oct 2, 2012||Teradata Us, Inc.||System for capturing a business context of a user's interaction with a website and method for the same|
|US8346803||Jan 27, 2009||Jan 1, 2013||Knapp Investment Company Limited||Dynamic generation of target files from template files and tracking of the processing of target files|
|US8352444||Jul 9, 2012||Jan 8, 2013||Peter Hon-You Chang||User-driven menu generation system with dynamic generation of target files with placeholders for persistent change or temporary security change over cloud computing virtual storage from template files|
|US8370495||Apr 1, 2010||Feb 5, 2013||Adaptive Computing Enterprises, Inc.||On-demand compute environment|
|US8381091||Sep 16, 2002||Feb 19, 2013||International Business Machines Corporation||Real-time method, system and program product for collecting web form data|
|US8386498||Aug 5, 2009||Feb 26, 2013||Loglogic, Inc.||Message descriptions|
|US8412719||Oct 7, 2009||Apr 2, 2013||Google Inc.||Method and system for segmenting a multidimensional dataset|
|US8417557||Jun 22, 2011||Apr 9, 2013||Webtrends, Inc.||Method and apparatus for evaluating visitors to a web server|
|US8429344 *||Oct 31, 2008||Apr 23, 2013||Fujitsu Limited||Storage apparatus, relay device, and method of controlling operating state|
|US8495198||Dec 16, 2011||Jul 23, 2013||Comscore, Inc.||Network resource monitoring and measurement system and method|
|US8543591||Oct 7, 2009||Sep 24, 2013||Google Inc.||Method and system for generating and sharing dataset segmentation schemes|
|US8549019||May 25, 2010||Oct 1, 2013||Google Inc.||Dynamically generating aggregate tables|
|US8549201||Jun 30, 2010||Oct 1, 2013||Intel Corporation||Interrupt blocker|
|US8554699||Oct 19, 2010||Oct 8, 2013||Google Inc.||Method and system for detecting anomalies in time series data|
|US8554804||Sep 15, 2011||Oct 8, 2013||Google Inc.||System and method for monitoring and analyzing internet traffic|
|US8583472 *||Sep 10, 2004||Nov 12, 2013||Fmr Llc||Measuring customer service levels|
|US8583584||Oct 19, 2010||Nov 12, 2013||Google Inc.||Method and system for using web analytics data for detecting anomalies|
|US8661111||Oct 25, 2000||Feb 25, 2014||The Nielsen Company (Us), Llc||System and method for estimating prevalence of digital content on the world-wide-web|
|US8682816||Sep 10, 2013||Mar 25, 2014||Google Inc.||Method and system for detecting anomalies in time series data|
|US8683324||Jan 11, 2008||Mar 25, 2014||Knapp Investment Company Limited||Dynamic generation of target files from template files and tracking of the processing of target files|
|US8700631 *||Nov 11, 2010||Apr 15, 2014||Trendit Ltd.||Tempo spatial data extraction from network connected devices|
|US8713025||Nov 20, 2011||Apr 29, 2014||Square Halt Solutions, Limited Liability Company||Complete context search system|
|US8713428||Dec 29, 2006||Apr 29, 2014||Comscore, Inc.||Content display monitor|
|US8719698||Nov 13, 2012||May 6, 2014||Comscore, Inc.||Content display monitor|
|US8751544||Sep 2, 2010||Jun 10, 2014||Google Inc.||Method and system for pivoting a multidimensional dataset|
|US8769394||Aug 3, 2010||Jul 1, 2014||Comscore, Inc.||Content display monitor|
|US8782120||May 2, 2011||Jul 15, 2014||Adaptive Computing Enterprises, Inc.||Elastic management of compute resources between a web server and an on-demand compute environment|
|US8793236||Nov 1, 2012||Jul 29, 2014||Adobe Systems Incorporated||Method and apparatus using historical influence for success attribution in network site activity|
|US8799643||Sep 14, 2012||Aug 5, 2014||The Nielsen Company (Us), Llc||System and method for monitoring secure data on a network|
|US8812462||Dec 20, 2012||Aug 19, 2014||Peter Hon-You Chang||User-driven menu generation system with dynamic generation of target files with placeholders for persistent change or temporary security change over cloud computing virtual storage from template files|
|US8819224 *||Jul 28, 2011||Aug 26, 2014||Bank Of America Corporation||Health and welfare monitoring of network server operations|
|US8924466||Oct 30, 2007||Dec 30, 2014||Level 3 Communications, Llc||Server handoff in content delivery network|
|US8930538||Mar 21, 2009||Jan 6, 2015||Level 3 Communications, Llc||Handling long-tail content in a content delivery network (CDN)|
|US8935382||Mar 16, 2009||Jan 13, 2015||Microsoft Corporation||Flexible logging, such as for a web server|
|US8972332||Oct 10, 2013||Mar 3, 2015||Google Inc.||Method and system for detecting anomalies in web analytics data|
|US8990378 *||Jul 5, 2007||Mar 24, 2015||Interwise Ltd.||System and method for collection and analysis of server log files|
|US9009728||Mar 6, 2007||Apr 14, 2015||Addthis, Inc.||Method and apparatus for widget and widget-container distribution control based on content rules|
|US9015324||Mar 13, 2012||Apr 21, 2015||Adaptive Computing Enterprises, Inc.||System and method of brokering cloud computing resources|
|US9058416||Dec 11, 2001||Jun 16, 2015||Peter K. Trzyna||System and method for detecting and reporting online activity using real-time content-based network monitoring|
|US9075657 *||Apr 7, 2006||Jul 7, 2015||Adaptive Computing Enterprises, Inc.||On-demand access to compute resources|
|US9081863||Dec 20, 2005||Jul 14, 2015||Adobe Systems Incorporated||One-click segmentation definition|
|US9112813||Feb 4, 2013||Aug 18, 2015||Adaptive Computing Enterprises, Inc.||On-demand compute environment|
|US9122715||Jun 29, 2006||Sep 1, 2015||International Business Machines Corporation||Detecting changes in end-user transaction performance and availability caused by changes in transaction server configuration|
|US9167036||Feb 14, 2002||Oct 20, 2015||Level 3 Communications, Llc||Managed object replication and delivery|
|US9171093||May 10, 2011||Oct 27, 2015||Adobe Systems Incorporated||User interface providing summary information or a status pane in a web analytics tool|
|US9183200 *||Aug 2, 2012||Nov 10, 2015||Symantec Corporation||Scale up deduplication engine via efficient partitioning|
|US9185016||Aug 14, 2013||Nov 10, 2015||Google Inc.||System and method for monitoring and analyzing internet traffic|
|US9185435||Jun 24, 2014||Nov 10, 2015||The Nielsen Company (Us), Llc||Methods and apparatus to characterize households with media meter data|
|US9231886||May 5, 2015||Jan 5, 2016||Adaptive Computing Enterprises, Inc.||Simple integration of an on-demand compute environment|
|US9277265||Feb 10, 2015||Mar 1, 2016||The Nielsen Company (Us), Llc||Methods and apparatus to calculate video-on-demand and dynamically inserted advertisement viewing probability|
|US9304956||Oct 1, 2013||Apr 5, 2016||Intel Corporation||Interrupt blocker|
|US9305105 *||May 25, 2010||Apr 5, 2016||Google Inc.||System and method for aggregating analytics data|
|US9401897||Aug 4, 2014||Jul 26, 2016||The Nielsen Company (Us), Llc.||System and method for monitoring secure data on a network|
|US9495084||Apr 13, 2015||Nov 15, 2016||Oracle International Corporation||Method and apparatus for widget and widget-container distribution control based on content rules|
|US9514479||Jan 6, 2014||Dec 6, 2016||The Nielsen Company (Us), Llc||System and method for estimating prevalence of digital content on the world-wide-web|
|US9544632||Feb 16, 2016||Jan 10, 2017||The Nielsen Company (Us), Llc||Methods and apparatus to calculate video-on-demand and dynamically inserted advertisement viewing probability|
|US9552433||Oct 17, 2014||Jan 24, 2017||Oracle International Corporation||Generic content collection systems|
|US20020040394 *||Aug 21, 2001||Apr 4, 2002||Webtrends Corporation||Data tracking using IP address filtering over a wide area network|
|US20020128925 *||Dec 11, 2001||Sep 12, 2002||Patrick Angeles||system and method for detecting and reporting online activity using real-time content-based network monitoring|
|US20020138762 *||Nov 30, 2001||Sep 26, 2002||Horne Donald R.||Management of log archival and reporting for data network security systems|
|US20020143925 *||Dec 29, 2000||Oct 3, 2002||Ncr Corporation||Identifying web-log data representing a single user session|
|US20020143933 *||Apr 3, 2001||Oct 3, 2002||International Business Machines Corporation||Clickstream data collection technique|
|US20020177907 *||May 25, 2001||Nov 28, 2002||International Business Machines Corporation||Method and apparatus for replaying and visualizing post-performance metrics for a complex heterogeneous data space|
|US20020188868 *||Jun 12, 2001||Dec 12, 2002||Budka Kenneth C.||Method for protecting use of resources in a network|
|US20020198684 *||Jun 22, 2001||Dec 26, 2002||Gross Kenny C.||Method and apparatus to facilitate measurement of quality-of-service performance of a network server|
|US20030018584 *||Jul 23, 2001||Jan 23, 2003||Cohen Jeremy Stein||System and method for analyzing transaction data|
|US20030105958 *||Dec 5, 2001||Jun 5, 2003||International Business Machines Corporation||Command script instrumentation for logging command execution and the protection of sensitive information|
|US20030131081 *||Dec 16, 2002||Jul 10, 2003||Krishnamohan Nareddy||Method and system for parsing navigation information|
|US20040046804 *||Aug 13, 2003||Mar 11, 2004||Chang Peter H.||User-driven menu generation system with multiple submenus|
|US20040054966 *||Sep 16, 2002||Mar 18, 2004||International Business Machines Corporation||Real-time method, system and program product for collecting web form data|
|US20040073644 *||Oct 15, 2002||Apr 15, 2004||Koch Donald O.||System and method for processing web activity data|
|US20040088407 *||Oct 17, 2003||May 6, 2004||Boyd William Glen||System and method for analyzing remote traffic data in a distributed computing environment|
|US20040128273 *||Dec 31, 2002||Jul 1, 2004||International Business Machines Corporation||Temporal link analysis of linked entities|
|US20040196311 *||Apr 22, 2004||Oct 7, 2004||Microsoft Corporation||Cluster-based visualization of user traffic on an internet site|
|US20040199445 *||Apr 9, 2004||Oct 7, 2004||Eder Jeff Scott||Business activity management system|
|US20040205575 *||Apr 12, 2002||Oct 14, 2004||Martin Wattenberg||Method and system for incorporating a value in a document|
|US20040225629 *||Nov 19, 2003||Nov 11, 2004||Eder Jeff Scott||Entity centric computer system|
|US20050114510 *||Nov 18, 2004||May 26, 2005||Error Brett M.||Assigning value to elements contributing to business success|
|US20050119900 *||Jun 12, 2002||Jun 2, 2005||Eder Jeff S.||Purchasing optimization system|
|US20050223093 *||Oct 15, 2004||Oct 6, 2005||Netiq Corporation||Systems and methods for tracking web activity|
|US20060059034 *||Sep 10, 2004||Mar 16, 2006||Iannucci Louis A||Measuring customer service levels|
|US20060085537 *||Oct 14, 2004||Apr 20, 2006||International Business Machines Corporation||Method and system for programmatically generating synthetic transactions to monitor performance and availability of a web application|
|US20060230149 *||Apr 7, 2006||Oct 12, 2006||Cluster Resources, Inc.||On-Demand Access to Compute Resources|
|US20060277198 *||Dec 20, 2005||Dec 7, 2006||Error Brett M||One-click segmentation definition|
|US20060277211 *||Dec 20, 2005||Dec 7, 2006||Error Brett M||Incrementally adding segmentation criteria to a data set|
|US20060277212 *||Dec 20, 2005||Dec 7, 2006||Error Brett M||User interface providing summary information or a status pane in a web analytics tool|
|US20080027841 *||Jan 16, 2002||Jan 31, 2008||Jeff Scott Eder||System for integrating enterprise performance management|
|US20080071859 *||Oct 31, 2007||Mar 20, 2008||Level 3 Communications, Llc||Popularity-based selective replication in content delivery network|
|US20080162542 *||Jan 11, 2008||Jul 3, 2008||Peter Hon-You Chang|
|US20080208947 *||Aug 23, 2007||Aug 28, 2008||Webtrends Corporation||Method and appratus for evaluating visitors to a web server|
|US20080222232 *||Mar 6, 2007||Sep 11, 2008||Allen Stewart O||Method and Apparatus for Widget and Widget-Container Platform Adaptation and Distribution|
|US20080235075 *||Mar 23, 2007||Sep 25, 2008||Fmr Corp.||Enterprise application performance monitors|
|US20080294743 *||Feb 15, 2008||Nov 27, 2008||Fuji Xerox Co., Ltd.||Information processing device, computer readable recording medium, and information processing method|
|US20090013007 *||Jul 5, 2007||Jan 8, 2009||Interwise Ltd.||System and Method for Collection and Analysis of Server Log Files|
|US20090112976 *||Oct 29, 2007||Apr 30, 2009||Hutchinson Kevin P||Method for measuring web traffic|
|US20090112977 *||Oct 30, 2007||Apr 30, 2009||Hutchinson Kevin P||System for measuring web traffic|
|US20090183117 *||Jan 27, 2009||Jul 16, 2009||Peter Hon-You Chang|
|US20090204760 *||Oct 31, 2008||Aug 13, 2009||Fujitsu Limited||Storage apparatus, relay device, and method of controlling operating state|
|US20100217767 *||May 5, 2010||Aug 26, 2010||Webtrends Inc.||System and method for analyzing remote traffic data in a distributed computing environment|
|US20100235494 *||Mar 16, 2009||Sep 16, 2010||Microsoft Corporation||Flexible logging, such as for a web server|
|US20100281389 *||Jul 2, 2010||Nov 4, 2010||Hutchinson Kevin P||System for measuring web traffic|
|US20100299434 *||Jul 28, 2010||Nov 25, 2010||Webtrends, Inc.||Systems and methods for tracking web activity|
|US20100306363 *||May 26, 2009||Dec 2, 2010||Erwien Saputra||Determining completion of a web server download session at a database server|
|US20100312884 *||May 25, 2010||Dec 9, 2010||Sagnik Nandy||System and method for aggregating analytics data|
|US20100318527 *||May 25, 2010||Dec 16, 2010||Sagnik Nandy||Dynamically generating aggregate tables|
|US20110035390 *||Aug 5, 2009||Feb 10, 2011||Loglogic, Inc.||Message Descriptions|
|US20110055214 *||Sep 2, 2010||Mar 3, 2011||Lik Mui||Method and System for Pivoting a Multidimensional Dataset|
|US20110055216 *||Nov 11, 2010||Mar 3, 2011||Trendit Ltd.||Tempo spatial data extraction from network connected devices|
|US20110055250 *||Oct 7, 2009||Mar 3, 2011||Sagnik Nandy||Method and system for generating and sharing dataset segmentation schemes|
|US20110119100 *||Oct 19, 2010||May 19, 2011||Jan Matthias Ruhl||Method and System for Displaying Anomalies in Time Series Data|
|US20110119226 *||Oct 19, 2010||May 19, 2011||Jan Matthias Ruhl||Method and System for Detecting Anomalies in Web Analytics Data|
|US20110119374 *||Oct 19, 2010||May 19, 2011||Jan Matthias Ruhl||Method and System for Detecting Anomalies in Time Series Data|
|US20110209067 *||Feb 19, 2010||Aug 25, 2011||Bogess Keandre||System and Method for Website User Valuation|
|US20110225287 *||Mar 12, 2010||Sep 15, 2011||Webtrends Inc.||Method and system for distributed processing of web traffic analytics data|
|US20110225288 *||Mar 12, 2010||Sep 15, 2011||Webtrends Inc.||Method and system for efficient storage and retrieval of analytics data|
|WO2002048830A2 *||Dec 11, 2001||Jun 20, 2002||Phlair, Inc.||System and method for detecting and reporting online activity using real-time content-based network monitoring|
|WO2002048830A3 *||Dec 11, 2001||Feb 27, 2003||Phlair Inc||System and method for detecting and reporting online activity using real-time content-based network monitoring|
|U.S. Classification||709/224, 714/E11.204|
|International Classification||H04L12/24, G06F11/34, H04L29/08, H04L29/06|
|Cooperative Classification||H04L67/1002, G06F11/3476, H04L41/00, H04L12/24, H04L2029/06054|
|European Classification||H04L41/00, H04L12/24, G06F11/34T4|
|Oct 26, 1998||AS||Assignment|
Owner name: WEBTRENDS CORPORATION, OREGON
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:BOYD, WILLIAM GLEN;SHAPIRA, ELIJAHU;REEL/FRAME:009549/0753
Effective date: 19981016
|Jul 18, 2002||AS||Assignment|
Owner name: NETIQ CORPORATION, CALIFORNIA
Free format text: MERGER;ASSIGNOR:WEBTRENDS CORPORATION;REEL/FRAME:012903/0384
Effective date: 20020614
|May 4, 2005||AS||Assignment|
Owner name: WEBTRENDS, INC., CALIFORNIA
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:NETIQ CORPORATION;REEL/FRAME:016522/0700
Effective date: 20050429
|May 5, 2005||AS||Assignment|
Owner name: WELLS FARGO FOOTHILL, INC., CALIFORNIA
Free format text: SECURITY AGREEMENT;ASSIGNOR:WEBTRENDS INC.;REEL/FRAME:015972/0647
Effective date: 20050429
|May 12, 2005||FPAY||Fee payment|
Year of fee payment: 4
|Apr 29, 2009||FPAY||Fee payment|
Year of fee payment: 8
|May 20, 2011||AS||Assignment|
Owner name: SILICON VALLEY BANK, OREGON
Free format text: SECURITY AGREEMENT;ASSIGNOR:WEBTRENDS INC.;REEL/FRAME:026319/0001
Effective date: 20110328
|May 8, 2013||FPAY||Fee payment|
Year of fee payment: 12
|Mar 16, 2017||AS||Assignment|
Owner name: WEBTRENDS INC., OREGON
Free format text: RELEASE BY SECURED PARTY;ASSIGNOR:WELLS FARGO CAPITAL FINANCE, LLC;REEL/FRAME:041598/0987
Effective date: 20110331